pHusion: A robust and versatile toolset for automated detection and analysis of exocytosis.

Exocytosis is a fundamental process used by eukaryotes to regulate the composition of the plasma membrane and facilitate cell-cell communication. To investigate exocytosis in neuronal morphogenesis, previously we developed computational tools with a graphical user interface to enable the automatic detection and analysis of exocytic events from fluorescence timelapse images. Though these tools were useful, we found the code was brittle and not easily adapted to different experimental conditions. Here we developed and validated a robust and versatile toolkit, named pHusion, for the analysis of exocytosis written in ImageTank, a graphical programming language that combines image visualization and numerical methods. We tested this method using a variety of imaging modalities and pH-sensitive fluorophores, diverse cell types, and various exocytic markers to generate a flexible and intuitive package. We show that VAMP3-mediated exocytosis occurs 30-times more frequently in melanoma cells compared with primary oligodendrocytes, that VAMP2-mediated fusion events in mature rat hippocampal neurons are longer lasting than those in immature murine cortical neurons, and that exocytic events are clustered in space yet random in time in developing cortical neurons.

In the Murine and Bovine Maternal Mammary Gland Signal Transducer and Activator of Transcription 3 is Activated in Clusters of Epithelial Cells around the Day of Birth.

Signal transducers and activators of transcription (STAT) proteins regulate mammary development. Here we investigate the expression of phosphorylated STAT3 (pSTAT3) in the mouse and cow around the day of birth. We present localised colocation analysis, applicable to other mammary studies requiring identification of spatially congregated events. We demonstrate that pSTAT3-positive events are multifocally clustered in a non-random and statistically significant fashion. Arginase-1 expressing cells, consistent with macrophages, exhibit distinct clustering within the periparturient mammary gland. These findings represent a new facet of mammary STAT3 biology, and point to the presence of mammary sub-microenvironments.

Unravelling the dynamics of mental health inequalities in England: A 12-year nationwide longitudinal spatial analysis of recorded depression prevalence.

Background: Depression is one of the most significant public health issues, but evidence of geographic patterns and trends of depression is limited. We aimed to examine the spatio-temporal patterns and trends of depression prevalence among adults in a nationwide longitudinal spatial study in England and evaluate the influence of neighbourhood socioeconomic deprivation in explaining patterns. Methods: Information on recorded depression prevalence was obtained from the indicator Quality and Outcomes Framework: Depression prevalence that measured the annual percentage of adults diagnosed with depression for Lower Super Output Areas (LSOA) from 2011 to 2022. We applied Cluster and Outlier Analysis using the Local Moran's I algorithm. Local effects of deprivation on depression in 2020 examined with Geographically Weighted Regression (GWR). Inequalities in recorded prevalence were presented using Prevalence Rate Ratios (PRR). Results: The North West Region of England had the highest concentration of High-High clusters of depression, with 17.4% of the area having high values surrounded by high values in both space and time and the greatest percentage of areas with a high rate of increase (43.1%). Inequalities widened among areas with a high rate of increase in prevalence compared to those with a lower rate of increase, with the PRR increasing from 1.66 (99% CI 1.61-1.70) in 2011 to 1.81 (99% CI 1.76-1.85) by 2022. Deprivation explained 3%-39% of the variance in depression in 2020 across the country. Conclusions: It is crucial to monitor depression's spatial patterns and trends and investigate mechanisms of mental health inequalities. Our findings can help identify priority areas and target prevention and intervention strategies in England. Evaluating mental health interventions in different geographic contexts can provide valuable insights to policymakers on the most effective and context-sensitive strategies, enabling them to allocate resources towards preventing the worsening of mental health inequalities.

Spatial Bayesian distributed lag non-linear models (SB-DLNM) for small-area exposure-lag-response epidemiological modelling.

BACKGROUND: Distributed lag non-linear models (DLNMs) are the reference framework for modelling lagged non-linear associations. They are usually used in large-scale multi-location studies. Attempts to study these associations in small areas either did not include the lagged non-linear effects, did not allow for geographically-varying risks or downscaled risks from larger spatial units through socioeconomic and physical meta-predictors when the estimation of the risks was not feasible due to low statistical power. METHODS: Here we proposed spatial Bayesian DLNMs (SB-DLNMs) as a new framework for the estimation of reliable small-area lagged non-linear associations, and demonstrated the methodology for the case study of the temperature-mortality relationship in the 73 neighbourhoods of the city of Barcelona. We generalized location-independent DLNMs to the Bayesian framework (B-DLNMs), and extended them to SB-DLNMs by incorporating spatial models in a single-stage approach that accounts for the spatial dependence between risks. RESULTS: The results of the case study highlighted the benefits of incorporating the spatial component for small-area analysis. Estimates obtained from independent B-DLNMs were unstable and unreliable, particularly in neighbourhoods with very low numbers of deaths. SB-DLNMs addressed these instabilities by incorporating spatial dependencies, resulting in more plausible and coherent estimates and revealing hidden spatial patterns. In addition, the Bayesian framework enriches the range of estimates and tests that can be used in both large- and small-area studies. CONCLUSIONS: SB-DLNMs account for spatial structures in the risk associations across small areas. By modelling spatial differences, SB-DLNMs facilitate the direct estimation of non-linear exposure-response lagged associations at the small-area level, even in areas with as few as 19 deaths. The manuscript includes an illustrative code to reproduce the results, and to facilitate the implementation of other case studies by other researchers.

Combined diffusion tensor imaging and quantitative susceptibility mapping to characterize normal-appearing white matter in self-limited epilepsy with centrotemporal spikes.

PURPOSE: In brain development, Myelination is the characteristic feature of white matter maturation, which plays an important role in efficient information transmitting. The white matter abnormality has been reported to be associated with self-limited epilepsy with centrotemporal spikes (SeLECTS). This study aimed to detect the altered white matter region in the SeLECTS patients by the combination of diffusion tensor imaging (DTI) and quantitative susceptibility mapping (QSM) technique. METHODS: 27 children with SeLECTS and 23 age- and gender-matched healthy children were enrolled. All participants were scanned with 3.0-T MRI to acquire the structure, diffusion and susceptibility-weighted data. The susceptibility and diffusion weighted data were processed to obtain quantitative susceptibility map and fraction anisotropy (FA) map. Then voxel-wise tract-based spatial statistics (TBSS) were used to analyze quantitative susceptibility and FA data. RESULTS: Both DTI and QSM revealed extensive white matter alterations in the frontal, parietal, and temporal lobes in SeLECTS patients. The overlapped region of DTI and QSM analyses was located in the fiber tracts of the corona radiata. The FA values in this overlapped region were negatively correlated with the magnetic susceptibility values. CONCLUSION: Our results suggest that TBSS-based QSM can be employed as a novel approach for characterizing alterations in white matter in SeLECTS. And the combination of QSM and DTI can provide a more comprehensive evaluation of white matter integrity by utilizing different biophysical features.

Brain structure variation and individual differences in theory of mind among older adults.

The theory of mind (ToM) is not substantially influenced by aging, suggesting the emergence of various compensatory mechanisms. To identify brain regions subserving ToM in older adults, we investigated the associations of individual differences in brain structure with performance on the Reading the Mind in the Eyes Test (RMET), a widely used measure of ToM, using voxel-based morphometry (VBM) and tract-based spatial statistics (TBSS). In contrast to findings obtained from young adults, where multiple cortical regions are implicated in ToM, VBM analysis revealed a significant positive correlation between RMET score and gray matter (GM) volume only in the right middle temporal gyrus, a region implicated in social cognition. Alternatively, TBSS revealed significant positive correlations between RMET score and the fractional anisotropy (FA) values in widespread white matter (WM) tracts, including the bilateral uncinate fasciculus, a region previously linked to RMET performance in young adults. We speculate that individual differences in WM integrity are strong influences on ToM among older adults, whereas the impact of individual differences in GM volumes is relatively limited.

Alterations in the white matter structure of major depressive disorder patients and their link to childhood trauma.

Objectives: Major Depressive Disorder (MDD) is significantly influenced by childhood trauma (CT), affecting brain anatomy and functionality. Despite the unique disease trajectory in MDD patients with CT, the underlying neurobiological mechanisms remain unclear. Our objective is to investigate CT's impact on the white matter structure of the brain in patients with MDD. Methods: This research employed tract-based spatial statistics (TBSS) to detect variations between groups in Fractional Anisotropy (FA) throughout the whole brain in 71 medication-free MDD patients and 97 HCs. Participants filled out the Childhood Trauma Questionnaire (CTQ) and assessments for depression and anxiety symptoms. The relationship between FA and CTQ scores was explored with partial correlation analysis, adjusting for factors such as age, gender, educational background, and length of illness. Results: Compared to HCs, the MDD group showed decreased FA values in the right posterior limb of the internal capsule (PLIC), the inferior fronto-occipital fasciculus (IFOF), and bilateral superior longitudinal fasciculus (SLF). Simple effects analysis revealed that compared to HC-CT, the MDD-CT group demonstrated decreased FA values in right PLIC, IFOF, and bilateral SLF. The MDD-nCT group showed decreased FA values in right PLIC and IFOF compared to HC-nCT. The total scores and subscale scores of CTQ were negatively correlated with the FA in the right SLF. Conclusion: The right SLF may potentially be influenced by CT during the brain development of individuals with MDD. These results enhance our knowledge of the role of the SLF in the pathophysiology of MDD and the neurobiological mechanisms by which CT influences MDD.

Identifying local foci of tuberculosis transmission in Moldova using a spatial multinomial logistic regression model.

BACKGROUND: Multidrug resistant tuberculosis (MDR-TB) represents a major public health concern in the Republic of Moldova, with an estimated 31% of new and 56% of previously treated TB cases having MDR disease in 2022. A recent genomic epidemiology study of incident TB occurring in 2018 and 2019 found that 92% of MDR-TB was the result of transmission. The MDR phenotype was concentrated among two M. tuberculosis (Mtb) lineages: L2.2.1 (Beijing) and L4.2.1 (Ural). METHODS: We developed and applied a hierarchical Bayesian multinominal logistic regression model to Mtb genomic, spatial, and epidemiological data collected from all individuals with diagnosed TB in Moldova in 2018 and 2019 to identify locations in which specific Mtb strains are being transmitted. We then used a logistic regression model to estimate locality-level factors associated with local transmission. FINDINGS: We found differences in the spatial distribution and degree of local concentration of disease due to specific strains of Beijing and Ural lineage Mtb. Foci of transmission for four strains of Beijing lineage Mtb, predominantly of the MDR-TB phenotype, were located in several regions, but largely concentrated in Transnistria. In contrast, transmission of Ural lineage Mtb had less marked patterns of spatial aggregation, with a single strain (also of the MDR phenotype) spatially clustered in southern Transnistria. We found a 30% (95% credible interval 2%-80%) increase in odds of a locality being a transmission cluster for each increase of 100 persons per square kilometer, while higher local tuberculosis incidence and poverty were not associated with a locality being a transmission focus. INTERPRETATION: Our results identified localities where specific Mtb transmission networks were concentrated and quantified the association between locality-level factors and focal transmission. This analysis revealed Transnistria as the primary area where specific Mtb strains (predominantly of the MDR-TB phenotype) were locally transmitted and suggests that targeted intensified case finding in this region may be an attractive policy option. FUNDING: Funding for this work was provided by the National Institute of Allergy and Infectious Diseases at the US National Institutes of Health.

Extended correlation functions for spatial analysis of multiplex imaging data.

Imaging platforms for generating highly multiplexed histological images are being continually developed and improved. Significant improvements have also been made in the accuracy of methods for automated cell segmentation and classification. However, less attention has focused on the quantification and analysis of the resulting point clouds, which describe the spatial coordinates of individual cells. We focus here on a particular spatial statistical method, the cross-pair correlation function (cross-PCF), which can identify positive and negative spatial correlation between cells across a range of length scales. However, limitations of the cross-PCF hinder its widespread application to multiplexed histology. For example, it can only consider relations between pairs of cells, and cells must be classified using discrete categorical labels (rather than labeling continuous labels such as stain intensity). In this paper, we present three extensions to the cross-PCF which address these limitations and permit more detailed analysis of multiplex images: topographical correlation maps can visualize local clustering and exclusion between cells; neighbourhood correlation functions can identify colocalization of two or more cell types; and weighted-PCFs describe spatial correlation between points with continuous (rather than discrete) labels. We apply the extended PCFs to synthetic and biological datasets in order to demonstrate the insight that they can generate.

Spatiotemporal Patterns of Reptile and Amphibian Road Fatalities in a Natura 2000 Area: A 12-Year Monitoring of the Lake Karla Mediterranean Wetland.

The pervasive expansion of human-engineered infrastructure, particularly roads, has fundamentally reshaped landscapes, profoundly affecting wildlife interactions. Wildlife-vehicle collisions, a common consequence of this intricate interplay, frequently result in fatalities, extending their detrimental impact within Protected Areas (PAs). Among the faunal groups most susceptible to road mortality, reptiles and amphibians stand at the forefront, highlighting the urgent need for global comprehensive mitigation strategies. In Greece, where road infrastructure expansion has encroached upon a significant portion of the nation's PAs, the plight of these road-vulnerable species demands immediate attention. To address this critical issue, we present a multifaceted and holistic approach to investigating and assessing the complex phenomenon of herpetofauna road mortality within the unique ecological context of the Lake Karla plain, a rehabilitated wetland complex within a PA. To unravel the intricacies of herpetofauna road mortality in the Lake Karla plain, we conducted a comprehensive 12-year investigation from 2008 to 2019. Employing a combination of statistical modeling and spatial analysis techniques, we aimed to identify the species most susceptible to these encounters, their temporal and seasonal variations, and the ecological determinants of their roadkill patterns. We documented a total of 340 roadkill incidents involving 14 herpetofauna species in the Lake Karla's plain, with reptiles, particularly snakes, being more susceptible, accounting for over 60% of roadkill occurrences. Moreover, we found that environmental and road-related factors play a crucial role in influencing roadkill incidents, while spatial analysis techniques, including Kernel Density Estimation, the Getis-Ord Gi*, and the Kernel Density Estimation plus methods revealed critical areas, particularly in the south-eastern region of Lake Karla's plain, offering guidance for targeted interventions to address both individual and collective risks associated with roadkill incidents.

Spatial Pattern Analysis using Closest Events (SPACE)-A Nearest Neighbor Point Pattern Analysis Framework for Assessing Spatial Relationships from Digital Images.

The quantitative description of biological structures is a valuable yet difficult task in the life sciences. This is commonly accomplished by imaging samples using fluorescence microscopy and analyzing resulting images using Pearson's correlation or Manders' co-occurrence intensity-based colocalization paradigms. Though conceptually and computationally simple, these approaches are critically flawed due to their reliance on signal overlap, sensitivity to cursory signal qualities, and inability to differentiate true and incidental colocalization. Point pattern analysis provides a framework for quantitative characterization of spatial relationships between spatial patterns using the distances between observations rather than their overlap, thus overcoming these issues. Here we introduce an image analysis tool called Spatial Pattern Analysis using Closest Events (SPACE) that leverages nearest neighbor-based point pattern analysis to characterize the spatial relationship of fluorescence microscopy signals from image data. The utility of SPACE is demonstrated by assessing the spatial association between mRNA and cell nuclei from confocal images of cardiac myocytes. Additionally, we use synthetic and empirical images to characterize the sensitivity of SPACE to image segmentation parameters and cursory image qualities such as signal abundance and image resolution. Ultimately, SPACE delivers performance superior to traditional colocalization methods and offers a valuable addition to the microscopist's toolbox.

Modeling variation in mixture effects over space with a Bayesian spatially varying mixture model.

Mixture analysis is an emerging statistical tool in epidemiological research that seeks to estimate the health effects associated with mixtures of several exposures. This approach acknowledges that individuals experience many simultaneous exposures and it can estimate the relative importance of components in the mixture. Health effects due to mixtures may vary over space driven by to political, demographic, environmental, or other differences. In such cases, estimating a global mixture effect without accounting for spatial variation would induce bias in effect estimates and potentially lower statistical power. To date, no methods have been developed to estimate spatially varying chemical mixture effects. We developed a Bayesian spatially varying mixture model that estimates spatially varying mixture effects and the importance weights of components in the mixture, while adjusting for covariates. We demonstrate the efficacy of the model through a simulation study that varies the number of mixtures (one and two) and spatial pattern (global, one-dimensional, radial) and magnitude of mixture effects, showing that the model is able to accurately reproduce the spatial pattern of mixture effects across a diverse set of scenarios. Finally, we apply our model to a multi-center case-control study of non-Hodgkin lymphoma (NHL) in Detroit, Iowa, Los Angeles, and Seattle. We identify significant spatially varying positive and inverse associations with NHL for two mixtures of pesticides in Iowa and do not find strong spatial effects at the other three centers. In conclusion, the Bayesian spatially varying mixture model represents a novel method for modeling spatial variation in mixture effects.

White matter microstructural alterations in patients with anti-N-methyl-D-aspartate receptor encephalitis: A tract-based spatial statistics study.

BACKGROUND: Cognitive impairment is common in patients with anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis; however, neural mechanisms underlying this impairment remain unclear. Diffusion tensor imaging (DTI) is a potential method for studying the condition of white matter fibers in patients with anti-NMDAR encephalitis, allowing for an analysis of the neuroimaging mechanisms of cognitive impairment in conjunction with cognitive scales. This study aimed to explore white matter microstructural alterations and their correlation with cognitive function in patients with anti-NMDAR encephalitis. METHODS: DTI data were collected from 22 patients with anti-NMDAR encephalitis (aged 29.00(19.75, 39.50) years; 12 males, 10 females) and 20 healthy controls (HCs) (aged 24.50(21.25, 32.00); 12 males, 8 females) matched for age, sex, and educational level. Changes in the white matter microstructure were analyzed using tract-based spatial statistics. Pearson correlation analysis was used to explore the correlation between white matter integrity and neuropsychological scores. RESULTS: Compared with HCs, patients with anti-NMDAR encephalitis showed decreased fractional anisotropy and increased mean diffusivity values in extensive white matter regions, which were associated with disease severity, memory, and executive and visuospatial functions. CONCLUSION: Widespread impairment of the structural integrity of the white matter in the brain is significantly associated with cognitive dysfunction in patients with anti-NMDAR encephalitis, providing neuroimaging evidence for studying the underlying mechanisms.

Spatial confidence regions for combinations of excursion sets in image analysis.

The analysis of excursion sets in imaging data is essential to a wide range of scientific disciplines such as neuroimaging, climatology, and cosmology. Despite growing literature, there is little published concerning the comparison of processes that have been sampled across the same spatial region but which reflect different study conditions. Given a set of asymptotically Gaussian random fields, each corresponding to a sample acquired for a different study condition, this work aims to provide confidence statements about the intersection, or union, of the excursion sets across all fields. Such spatial regions are of natural interest as they directly correspond to the questions 'Where do all random fields exceed a predetermined threshold?', or 'Where does at least one random field exceed a predetermined threshold?'. To assess the degree of spatial variability present, our method provides, with a desired confidence, subsets and supersets of spatial regions defined by logical conjunctions (i.e. set intersections) or disjunctions (i.e. set unions), without any assumption on the dependence between the different fields. The method is verified by extensive simulations and demonstrated using task-fMRI data to identify brain regions with activation common to four variants of a working memory task.

Microstructural White Matter Abnormalities in Children and Adolescents With Narcolepsy Type 1.

BACKGROUND: In 2010, the H1N1 Pandemrix vaccination campaign was followed by a sudden increase in narcolepsy type 1 (NT1). We investigated the brain white matter microstructure in children with onset of NT1 within two years after the Pandemrix vaccination. METHODS: We performed diffusion-weighted magnetic resonance imaging (MRI) on 19 children and adolescents with NT1 and 19 healthy controls. Imaging was performed at a median of 4 years after the diagnosis at a median age of 16 years. For the MRI, we used whole-brain tractography and tract-based spatial statistics (TBSS). We compared these results with medical records and questionnaire data. RESULTS: Narcoleptic children showed a global decrease in mean, axial, and radial diffusivity and an increase in planarity coefficient in the white matter TBSS skeleton and tractography. These differences were widespread, and there was an increased asymmetry of the mean diffusivity in children with NT1. The global microstructural metrics were reflected in behavior, and especially the axial diffusion levels correlated with anxiety and depression symptoms and social and behavioral problems. CONCLUSIONS: In pediatric patients with Pandemrix-associated NT1, several global changes in the brain white matter network skeleton were observed within five years after the onset of NT1. The degree of changes correlates with behavioral problems.

An Introduction to Bayesian Spatial Smoothing Methods for Disease Mapping: Modeling County Firearm Suicide Mortality Rates.

This article introduces Bayesian spatial smoothing models for disease mapping, a specific application of small area estimation where the full universe of data is known, to a wider audience of public health professionals using firearm suicide as a motivating example. Besag, York and Mollié (BYM) Poisson spatial and space-time smoothing models were fit to firearm suicide counts for the years 2014-2018. County raw death rates in 2018 ranged from 0-24.81 deaths per 10,000 people. However, the highest mortality rate was highly unstable based on only 2 deaths in a population of approximately 800, and 82.4% of contiguous US counties experienced fewer than 10 firearm suicide deaths and were thus suppressed. Spatially smoothed county firearm suicide mortality estimates ranged from 0.06-4.05 deaths per 10,000 people and could be reported for all counties. The space-time smoothing model produced similar estimates with narrower credible intervals as it allowed counties to gained precision from adjacent neighbors and their own rates in adjacent years. Bayesian spatial smoothing methods are a useful tool for evaluating spatial health disparities in small geographies where small numbers can result in highly variable rate estimates, and new estimation techniques in R have made fitting these models more accessible to researchers.

Migration timing and marine space use of an anadromous Arctic fish (Arctic Char, Salvelinus alpinus) revealed by local spatial statistics and network analysis.

BACKGROUND: The ice-free season (typically late-June to early-October) is crucial for anadromous species of fish in the Arctic, including Arctic Char (Salvelinus alpinus), which must acquire adequate resources for growth, reproduction, and survival during a brief period of feeding in the marine environment. Arctic Char is an important food fish for Inuit communities across the Arctic. Understanding drivers and patterns of migration in the marine environment is thus essential for conservation and management of the species. METHODS: We used passive acoustic telemetry to characterize migration patterns of 51 individual anadromous Arctic Char during the ice-free season in the marine environment of Coronation Gulf (Nunavut, Canada; 2019-2022). Based on recent genetic evidence, some tagged individuals were likely Dolly Varden (Salvelinus malma malma), a closely related species to Arctic Char. Using local Getis G* and network analysis, we described movement patterns and identified high-use locations in the marine environment. We also related freshwater overwintering location to migration timing and movement pattern. RESULTS: Comparing groups of fish that overwintered in distinct locations, we found: (i) limited evidence that marine movements were associated with overwintering location; (ii) minor differences in use of marine space; and, (iii) timing of freshwater return differed significantly between overwintering groups, and was related to length and difficulty of the migratory pathway in freshwater. Results from both network analysis and local Getis G* revealed that, regardless of overwintering location, coastal locations were highly used by fish. CONCLUSIONS: Overwintering locations, and the migratory routes to access overwintering locations, affect the timing of freshwater return. Preference of fish for coastal marine locations is likely due to abundance of forage and patterns in break-up of sea ice. Similarities in marine space use and movement patterns present challenges for managing this and other mixed stock fisheries of anadromous Salvelinus spp. Absences or periods of time when fish were not detected prevented comprehensive assessment of movement patterns. Local Getis G*, a local indicator of spatial association, is a helpful tool in identifying locations associated with absences in acoustic telemetry arrays, and is a complementary method to network analysis.

Exploring White Matter Abnormalities in Young Children with Autism Spectrum Disorder: Integrating Multi-shell Diffusion Data and Machine Learning Analysis.

RATIONALE AND OBJECTIVES: This study employed tract-based spatial statistics (TBSS) to investigate abnormalities in the white matter microstructure among children with autism spectrum disorder (ASD). Additionally, an eXtreme Gradient Boosting (XGBoost) model was developed to effectively classify individuals with ASD and typical developing children (TDC). METHODS AND MATERIALS: Multi-shell diffusion weighted images were acquired from 62 children with ASD and 44 TDC. Using the Pydesigner procedure, diffusion tensor (DT), diffusion kurtosis (DK), and white matter tract integrity (WMTI) metrics were computed. Subsequently, TBSS analysis was applied to discern differences in these diffusion parameters between ASD and TDC groups. The XGBoost model was then trained using metrics showing significant differences, and Shapley Additive explanations (SHAP) values were computed to assess the feature importance in the model's predictions. RESULTS: TBSS analysis revealed a significant reduction in axonal diffusivity (AD) in the left posterior corona radiata and the right superior corona radiata. Among the DK indicators, mean kurtosis, axial kurtosis, and kurtosis fractional anisotropy were notably increased in children with ASD, with no significant difference in radial kurtosis. WMTI metrics such as axonal water fraction, axonal diffusivity of the extra-axonal space (EAS_AD), tortuosity of the extra-axonal space (EAS_TORT), and diffusivity of intra-axonal space (IAS_Da) were significantly increased, primarily in the corpus callosum and fornix. Notably, there was no significant difference in radial diffusivity of the extra-axial space (EAS_RD). The XGBoost model demonstrated excellent classification ability, and the SHAP analysis identified EAS_TORT as the feature with the highest importance in the model's predictions. CONCLUSION: This study utilized TBSS analyses with multi-shell diffusion data to examine white matter abnormalities in pediatric autism. Additionally, the developed XGBoost model showed outstanding performance in classifying ASD and TDC. The ranking of SHAP values based on the XGBoost model underscored the significance of features in influencing model predictions.

Microstructural white matter abnormalities in overactive bladder syndrome evaluation with diffusion kurtosis imaging tract-based spatial statistics analysis.

PURPOSE: This prospective study aimed to explore the microstructural alterations of the white matter in overactive bladder syndrome (OAB) using the Tract-based Spatial Statistics (TBSS) method of diffusion kurtosis imaging (DKI). METHODS: A total of 30 patients were enrolled and compared with 30 controls. White matter (WM) status was assessed using tract-based spatial statistics for DKI. The differences in DKI-derived parameters, including kurtosis fractional anisotropy (KFA), fractional anisotropy (FA), mean kurtosis (MK), mean diffusivity (MD), radial kurtosis (RK), axial kurtosis (AK), axial diffusivity (AD), and radial diffusivity (RD), were compared between the two groups using the TBSS method. The correlation between the altered DKI-derived parameters and the (OABSS) scores was analyzed. A receiver operating characteristic curve (ROC) was used to evaluate the diagnostic performance of different white matter parameters. RESULTS: As a result, compared with the HC group, the KFA, and FA values decreased significantly in the OAB group. Compared with the HC group, the MK and MD values increased significantly in the OAB group. The KFA values of the genu of corpus callosum (GCC) were significantly correlated with the OABSS scores (r = - 0.509; p = 0.004). The FA values of anterior corona radiata (ACR) were significantly correlated with OABSS scores (r = - 0.447; p = 0.013). The area under the ROC curve (AUC) for the genu of corpus callosum KFA values was higher than FA for the diagnosis of OAB patients. CONCLUSION: DKI is a promising approach to the investigation of the pathophysiology of OAB and a potential biomarker for clinical diagnosis of OAB.

Diffusion tensor imaging in photosensitive and nonphotosensitive juvenile myoclonic epilepsy.

INTRODUCTION/BACKGROUND: Juvenile myoclonic epilepsy (JME) syndrome is known to cause alterations in brain structure and white matter integrity. The study aimed to determine structural white matter changes in patients with JME and to reveal the differences between the photosensitive (PS) and nonphotosensitive (NPS) subgroups by diffusion tensor imaging (DTI) using the tract-based spatial statistics (TBSS) method. METHODS: This study included data from 16 PS, 15 NPS patients with JME, and 41 healthy participants. The mean fractional anisotropy (FA) values of these groups were calculated, and comparisons were made via the TBSS method over FA values in the whole-brain and 81 regions of interest (ROI) obtained from the John Hopkins University White Matter Atlas. RESULTS: In the whole-brain TBSS analysis, no significant differences in FA values were observed in pairwise comparisons of JME patient group and subgroups with healthy controls (HCs) and in comparison between JME subgroups. In ROI-based TBSS analysis, an increase in FA values of right anterior corona radiata and left corticospinal pathways was found in JME patient group compared with HC group. When comparing JME-PS patients with HCs, an FA increase was observed in the bilateral anterior corona radiata region, whereas when comparing JME-NPS patients with HCs, an FA increase was observed in bilateral corticospinal pathway. Moreover, in subgroup comparison, an increase in FA values was noted in corpus callosum genu region in JME-PS compared with JME-NPS. CONCLUSIONS: Our results support the disruption in thalamofrontal white matter integrity in JME, and subgroups and highlight the importance of using different analysis methods to show the underlying microstructural changes.